1. Technical Field of the Invention
This invention relates generally to the field of automated system monitoring and anomaly detection and, in particular, to methods of generating system monitoring knowledge bases from nominal system behavior, and to the use of these knowledge bases in monitoring system performance in real-time or near-real-time.
2. Description of the Prior Art
The modern information age provides great quantities of raw data concerning the performance of man-made engineered systems as well as data concerning the behavior of natural systems. Numerous information processing techniques have been employed to attempt to classify such data, look for anomalies, or otherwise assist humans to extract, understand and/or respond to information contained in the data. Examples of such techniques include model based reasoning, machine learning, neural networks, data mining, support vector machines, various decision tree models including ID3 decision tree learner, among many others. However, these techniques typically have one or more drawbacks that render them unsuitable or disfavored for some applications.
For example, model based reasoning and related techniques typically require a detailed engineering simulation of the system under study, often including expert knowledge of system behavior, detailed behavior of system components and subsystems, detailed knowledge of interaction among system components and failure mechanisms, among other knowledge. Such knowledge may not be available for all components and subsystems. Furthermore, even when a reasonably accurate system simulation is available, it often requires impractical amounts of computer resources. That is, the simulation may execute too slowly to provide information in real-time or near-real time so as to be unsuitable for many practical system monitoring applications. In addition, the computer resources may not be available in space-limited or weight-limited environments such as space vehicles. Thus, a need exists in the art for computationally rapid techniques to monitor the performance of a system and detect anomalous behavior without the need for excessive computer resources.
Some classification or decision models require that the system be trained with data that includes data derived from both normally-functioning systems (nominal data) as well as data derived from anomalous system behavior (off-nominal data). In many practical applications, off-nominal data is unavailable for training, and even the nominal data available for training may not fully explore all of the system's nominal operating regimes. Thus, a further need exists in the art for techniques to monitor a system's performance that does not require off-nominal data for training.